Results 41 to 50 of about 9,194 (254)

Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder

Sensors
Community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network.
Xiang Gu, Qiwei Huang, Jie Yang
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

SCGclust: Single-Cell Graph Clustering Using Graph Autoencoders That Integrate SNVs and CNAs

Mathematics
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognoses and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and the ...
Teja Potu, Yunfei Hu, Judy Wang, Hongmei Chi, Rituparna Khan, Srinija Dharani, Jingchao Ni, Liting Zhang, Xin Maizie Zhou, Xian Mallory   +9 more
doaj   +1 more source

SGVAE: Sequential Graph Variational Autoencoder

CoRR, 2019
Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process,
Bowen Jing, Ethan A. Chi, Jillian Tang
openaire   +3 more sources

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models

BMC Biology
Background Protein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation.
Feng Wang, Jinming Chu, Liyan Shen, Shan Chang   +3 more
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions

IEEE Access
As the popularity of cryptocurrencies grows, the threat of phishing scams on trading networks is growing. Detecting unusual transactions within the complex structure of these transaction graphs and imbalanced data between Benign and Scams remains a very ...
Junha Kang, Seok-Jun Buu
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Projection-based model-order reduction via graph autoencoders suited for unstructured meshes

Data-Centric Engineering
This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares Petrov–Galerkin (LSPG) projection scheme.
Liam Magargal, Parisa Khodabakhshi, Steven Rodriguez, Justin Jaworski, John Michopoulos   +4 more
doaj   +1 more source